Abstract

In this study, a variety of recent proposed spatial and spectral processing methods for hyperspectral imagery is reviewed and several important aspects of super-resolution problems and challenges are presented. The inherent variability in target and background spectra in hyperspectral imagery, the problem of high dimensionality of hyperspectral data in hyperspectral image classification, limitation in application of learning-based methods and the question of an optimal resolution factor for an arbitrary set of images, are some of the main challenges in this field.

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